Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Global spine journal

STUDY DESIGN : retrospective cohort study.

OBJECTIVES : To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery.

METHODS : The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent single level laminectomy in 2017 and 2018. The main outcome was ambulatory same day discharge. Study variables of interest included demographic information, comorbidities, preoperative laboratory values, and intra-operative information. Two machine learning predictive modeling algorithms, artificial neural network (ANN) and random forest, were trained to predict same day discharge. The quality of models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) measures.

RESULTS : Among 35,644 patients, 13,230 (37.1%) were discharged on the day of surgery. Both ANN and RF demonstrated a satisfactory model quality in terms of AUC (0.77 and 0.77), accuracy (0.69 and 0.70), sensitivity (0.83 and 0.58), specificity (0.55 and 0.80), PPV (0.77 and 0.69), and NPV (0.64 and 0.70). Both models highlighted several important predictive variables, including age, duration of operation, body mass index and preoperative laboratory values including, hematocrit, platelets, white blood cells, and alkaline phosphatase.

CONCLUSION : Machine learning approaches provide a promising tool to identify candidates for ambulatory laminectomy surgery. Both machine learning algorithms highlighted the as yet unrecognized importance of preoperative laboratory testing on patient pathway design.

Li Qiyi, Zhong Haoyan, Girardi Federico P, Poeran Jashvant, Wilson Lauren A, Memtsoudis Stavros G, Liu Jiabin

2021-Jan-07

ambulatory, artificial neural network, laminectomy, machine learning, random forest